!12 Develop model-fuzzing evaluation module.

Merge pull request !12 from jxlang910/master

example/mnist_demo/lenet5_mnist_coverage.py

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+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import sys
+import numpy as np
+
+from mindspore import Model
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
+from mindspore.nn import SoftmaxCrossEntropyWithLogits
+
+from mindarmour.attacks.gradient_method import FastGradientSignMethod
+from mindarmour.utils.logger import LogUtil
+from mindarmour.fuzzing.model_coverage_metrics import ModelCoverageMetrics
+
+from lenet5_net import LeNet5
+
+sys.path.append("..")
+from data_processing import generate_mnist_dataset
+
+LOGGER = LogUtil.get_instance()
+TAG = 'Neuron coverage test'
+LOGGER.set_level('INFO')
+
+
+def test_lenet_mnist_coverage():
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    # upload trained network
+    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
+    net = LeNet5()
+    load_dict = load_checkpoint(ckpt_name)
+    load_param_into_net(net, load_dict)
+    model = Model(net)
+
+    # get training data
+    data_list = "./MNIST_unzip/train"
+    batch_size = 32
+    ds = generate_mnist_dataset(data_list, batch_size, sparse=True)
+    train_images = []
+    for data in ds.create_tuple_iterator():
+        images = data[0].astype(np.float32)
+        train_images.append(images)
+    train_images = np.concatenate(train_images, axis=0)
+
+    # initialize fuzz test with training dataset
+    model_fuzz_test = ModelCoverageMetrics(model, 10000, 10, train_images)
+
+    # fuzz test with original test data
+    # get test data
+    data_list = "./MNIST_unzip/test"
+    batch_size = 32
+    ds = generate_mnist_dataset(data_list, batch_size, sparse=True)
+    test_images = []
+    test_labels = []
+    for data in ds.create_tuple_iterator():
+        images = data[0].astype(np.float32)
+        labels = data[1]
+        test_images.append(images)
+        test_labels.append(labels)
+    test_images = np.concatenate(test_images, axis=0)
+    test_labels = np.concatenate(test_labels, axis=0)
+    model_fuzz_test.test_adequacy_coverage_calculate(test_images)
+    LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
+    LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
+    LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
+
+    # generate adv_data
+    loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+    attack = FastGradientSignMethod(net, eps=0.3, loss_fn=loss)
+    adv_data = attack.batch_generate(test_images, test_labels, batch_size=32)
+    model_fuzz_test.test_adequacy_coverage_calculate(adv_data,
+                                                     bias_coefficient=0.5)
+    LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
+    LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
+    LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
+
+
+if __name__ == '__main__':
+    test_lenet_mnist_coverage()

mindarmour/fuzzing/__init__.py

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+from .model_coverage_metrics import ModelCoverageMetrics
+
+__all__ = ['ModelCoverageMetrics']

mindarmour/fuzzing/model_coverage_metrics.py

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+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Model-Test Coverage Metrics.
+"""
+
+import numpy as np
+
+from mindspore import Tensor
+from mindspore import Model
+
+from mindarmour.utils._check_param import check_model, check_numpy_param, \
+    check_int_positive
+
+
+class ModelCoverageMetrics:
+    """
+    Evaluate the testing adequacy of a model fuzz test.
+
+    Reference: `DeepGauge: Multi-Granularity Testing Criteria for Deep
+    Learning Systems <https://arxiv.org/abs/1803.07519>`_
+
+    Args:
+        model (Model): The pre-trained model which waiting for testing.
+        k (int): The number of segmented sections of neurons' output intervals.
+        n (int): The number of testing neurons.
+        train_dataset (numpy.ndarray): Training dataset used for determine
+            the neurons' output boundaries.
+    """
+
+    def __init__(self, model, k, n, train_dataset):
+        self._model = check_model('model', model, Model)
+        self._k = k
+        self._n = n
+        train_dataset = check_numpy_param('train_dataset', train_dataset)
+        self._lower_bounds = [np.inf]*n
+        self._upper_bounds = [-np.inf]*n
+        self._var = [0]*n
+        self._main_section_hits = [[0 for _ in range(self._k)] for _ in
+                                   range(self._n)]
+        self._lower_corner_hits = [0]*self._n
+        self._upper_corner_hits = [0]*self._n
+        self._bounds_get(train_dataset)
+
+    def _bounds_get(self, train_dataset, batch_size=32):
+        """
+        Update the lower and upper boundaries of neurons' outputs.
+
+        Args:
+            train_dataset (numpy.ndarray): Training dataset used for
+                determine the neurons' output boundaries.
+            batch_size (int): The number of samples in a predict batch.
+                Default: 32.
+        """
+        batch_size = check_int_positive('batch_size', batch_size)
+        output_mat = []
+        batches = train_dataset.shape[0] // batch_size
+        for i in range(batches):
+            inputs = train_dataset[i*batch_size: (i + 1)*batch_size]
+            output = self._model.predict(Tensor(inputs)).asnumpy()
+            output_mat.append(output)
+            lower_compare_array = np.concatenate(
+                [output, np.array([self._lower_bounds])], axis=0)
+            self._lower_bounds = np.min(lower_compare_array, axis=0)
+            upper_compare_array = np.concatenate(
+                [output, np.array([self._upper_bounds])], axis=0)
+            self._upper_bounds = np.max(upper_compare_array, axis=0)
+        self._var = np.std(np.concatenate(np.array(output_mat), axis=0),
+                           axis=0)
+
+    def _sections_hits_count(self, dataset, intervals):
+        """
+        Update the coverage matrix of neurons' output subsections.
+
+        Args:
+            dataset (numpy.ndarray): Testing data.
+            intervals (list[float]): Segmentation intervals of neurons'
+                outputs.
+        """
+        dataset = check_numpy_param('dataset', dataset)
+        batch_output = self._model.predict(Tensor(dataset)).asnumpy()
+        batch_section_indexes = (batch_output - self._lower_bounds) // intervals
+        for section_indexes in batch_section_indexes:
+            for i in range(self._n):
+                if section_indexes[i] < 0:
+                    self._lower_corner_hits[i] = 1
+                elif section_indexes[i] >= self._k:
+                    self._upper_corner_hits[i] = 1
+                else:
+                    self._main_section_hits[i][int(section_indexes[i])] = 1
+
+    def test_adequacy_coverage_calculate(self, dataset, bias_coefficient=0,
+                                         batch_size=32):
+        """
+        Calculate the testing adequacy of the given dataset.
+
+        Args:
+            dataset (numpy.ndarray): Data for fuzz test.
+            bias_coefficient (float): The coefficient used for changing the
+                neurons' output boundaries. Default: 0.
+            batch_size (int): The number of samples in a predict batch.
+                Default: 32.
+
+        Examples:
+            >>> model_fuzz_test = ModelCoverageMetrics(model, 10000, 10, train_images)
+            >>> model_fuzz_test.test_adequacy_coverage_calculate(test_images)
+        """
+        dataset = check_numpy_param('dataset', dataset)
+        batch_size = check_int_positive('batch_size', batch_size)
+        self._lower_bounds -= bias_coefficient*self._var
+        self._upper_bounds += bias_coefficient*self._var
+        intervals = (self._upper_bounds - self._lower_bounds) / self._k
+        batches = dataset.shape[0] // batch_size
+        for i in range(batches):
+            self._sections_hits_count(
+                dataset[i*batch_size: (i + 1)*batch_size], intervals)
+
+    def get_kmnc(self):
+        """
+        Get the metric of 'k-multisection neuron coverage'.
+
+        Returns:
+            float, the metric of 'k-multisection neuron coverage'.
+
+        Examples:
+            >>> model_fuzz_test.get_kmnc()
+        """
+        kmnc = np.sum(self._main_section_hits) / (self._n*self._k)
+        return kmnc
+
+    def get_nbc(self):
+        """
+        Get the metric of 'neuron boundary coverage'.
+
+        Returns:
+            float, the metric of 'neuron boundary coverage'.
+
+        Examples:
+            >>> model_fuzz_test.get_nbc()
+        """
+        nbc = (np.sum(self._lower_corner_hits) + np.sum(
+            self._upper_corner_hits)) / (2*self._n)
+        return nbc
+
+    def get_snac(self):
+        """
+        Get the metric of 'strong neuron activation coverage'.
+
+        Returns:
+            float: the metric of 'strong neuron activation coverage'.
+
+        Examples:
+            >>> model_fuzz_test.get_snac()
+        """
+        snac = np.sum(self._upper_corner_hits) / self._n
+        return snac

tests/ut/python/fuzzing/test_coverage_metrics.py

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+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Model-fuzz coverage test.
+"""
+import numpy as np
+import pytest
+
+from mindspore.train import Model
+import mindspore.nn as nn
+from mindspore.nn import Cell
+from mindspore import context
+from mindspore.nn import SoftmaxCrossEntropyWithLogits
+
+from mindarmour.attacks.gradient_method import FastGradientSignMethod
+from mindarmour.utils.logger import LogUtil
+from mindarmour.fuzzing.model_coverage_metrics import ModelCoverageMetrics
+
+LOGGER = LogUtil.get_instance()
+TAG = 'Neuron coverage test'
+LOGGER.set_level('INFO')
+
+
+# for user
+class Net(Cell):
+    """
+    Construct the network of target model.
+
+    Examples:
+        >>> net = Net()
+    """
+
+    def __init__(self):
+        """
+        Introduce the layers used for network construction.
+        """
+        super(Net, self).__init__()
+        self._relu = nn.ReLU()
+
+    def construct(self, inputs):
+        """
+        Construct network.
+
+        Args:
+            inputs (Tensor): Input data.
+        """
+        out = self._relu(inputs)
+        return out
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_card
+@pytest.mark.component_mindarmour
+def test_lenet_mnist_coverage_cpu():
+    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
+    # load network
+    net = Net()
+    model = Model(net)
+
+    # initialize fuzz test with training dataset
+    training_data = (np.random.random((10000, 10))*20).astype(np.float32)
+    model_fuzz_test = ModelCoverageMetrics(model, 10000, 10, training_data)
+
+    # fuzz test with original test data
+    # get test data
+    test_data = (np.random.random((2000, 10))*20).astype(np.float32)
+    test_labels = np.random.randint(0, 10, 2000).astype(np.int32)
+    model_fuzz_test.test_adequacy_coverage_calculate(test_data)
+    LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
+    LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
+    LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
+
+    # generate adv_data
+    loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+    attack = FastGradientSignMethod(net, eps=0.3, loss_fn=loss)
+    adv_data = attack.batch_generate(test_data, test_labels, batch_size=32)
+    model_fuzz_test.test_adequacy_coverage_calculate(adv_data,
+                                                     bias_coefficient=0.5)
+    LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
+    LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
+    LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_card
+@pytest.mark.component_mindarmour
+def test_lenet_mnist_coverage_ascend():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    # load network
+    net = Net()
+    model = Model(net)
+
+    # initialize fuzz test with training dataset
+    training_data = (np.random.random((10000, 10))*20).astype(np.float32)
+    model_fuzz_test = ModelCoverageMetrics(model, 10000, 10, training_data)
+
+    # fuzz test with original test data
+    # get test data
+    test_data = (np.random.random((2000, 10))*20).astype(np.float32)
+    test_labels = np.random.randint(0, 10, 2000)
+    test_labels = (np.eye(10)[test_labels]).astype(np.float32)
+    model_fuzz_test.test_adequacy_coverage_calculate(test_data)
+    LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
+    LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
+    LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
+
+    # generate adv_data
+    attack = FastGradientSignMethod(net, eps=0.3)
+    adv_data = attack.batch_generate(test_data, test_labels, batch_size=32)
+    model_fuzz_test.test_adequacy_coverage_calculate(adv_data,
+                                                     bias_coefficient=0.5)
+    LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
+    LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
+    LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())